Solving Sparse Assignment Problems on FPGAs

Abstract

The assignment problem is a fundamental optimization problem and a crucial part of many systems. For example, in multiple object tracking, the assignment problem is used to associate object detections with hypothetical target tracks and solving the assignment problem is one of the most compute-intensive tasks. To enable low-latency real-time implementations, efficient solutions to the assignment problem is required. In this work, we present Sparse and Speculative (SaS) Auction, a novel implementation of the popular Auction algorithm for FPGAs. Two novel optimizations are proposed. First, the pipeline width and depth are reduced by exploiting sparsity in the input problems. Second, dependency speculation is employed to enable a fully pipelined design and increase the throughput. Speedups as high as 50 × are achieved relative to the state-of-the-art implementation for some input distributions. We evaluate the implementation both on randomly generated datasets and realistic datasets from multiple object tracking.